The systems and methods of the invention relate to portfolio risk optimization.
Various techniques are known for portfolio optimization. Typically, the portfolio optimization problem is defined by maximizing a return measure while minimizing a risk measure given a set of constraints. For example, classical Markowitz portfolio theory has been widely used as a foundation for portfolio optimization. However, the framework has two major drawbacks that reduce its application to practical investment problems. First, due to the nonlinearity of the risk measure (variance), the optimization problem has to be solved by a nonlinear programming (NLP) optimizer. In a problem with high dimension, general purpose nonlinear optimizers cannot generate an optimal solution within a reasonable amount of time. Typically, problems with 30-50 asset classes reach the practical limit of a NLP optimizer. Portfolio managers may use mean-variance optimization to determine broad asset allocations, but these solutions then must be further evaluated to determine an investment strategy that can be implemented, and this process generally leads to suboptimal solutions. With very large portfolio values, even small degradations in solution quality can have a significant impact on the calculated return.
The second drawback deals with the risk measure. Variance measures the variation around mean. It is an accepted risk measure in a normal situation. Risk managers may also want to manage the portfolio to weather the occurrences of rare events with severe impact. Therefore, the downside risk, also called tail risk, has to be minimized. The variance measure does not provide sufficient information about the tail risk when the distribution is not symmetrical about its mean (e.g., in a non-normal distribution situation). Asymmetric return distributions are common in practice. Therefore, a third measure, in addition to return and variance, is required to account for tail risk.
For institutions with asset-liability management (ALM) constraints, e.g., insurance companies and banks, portfolio managers need to match the asset characteristics with those of liabilities. One of the most well studied risk factors is interest rates risk. In an immunization process, asset duration is approximately matched with liability duration to be within a pre-specified target duration mismatch range. Convexity is included in the analysis to improve accuracy. To further improve the analysis, key rate durations are used to capture the non-parallel movement of the yield curve.
In a traditional ALM optimization, the problem is formulated as:
Maximize Return Measure:
subject to (s.t.):
                Partial duration mismatches≦target;        Total duration mismatch≦target;        Total Convexity mismatch≦target; and        Other linear constraints.        
This optimization problem is currently solved using a Linear Programming (LP) optimizer as the objective function and the constraints are linear. However, this approach yields a sub-optimal solution because the problem formulation does not include a measure of the overall portfolio risk. Portfolio managers need to adjust a number of linear risk constraints to achieve the desired targets. Including the risk measure makes the problem nonlinear and unsolvable using an LP optimizer. In other words, the formulation does not provide portfolio managers full control over the portfolio total risk. They may use total duration as a proxy for the total risk and control the total duration mismatch while loosening the constraints on the key rate duration mismatches. Due to the theoretical drawbacks of the total duration measure, one can challenge the technical soundness of this approach.
The problem becomes worse when multiple risk factors are included in the portfolio analysis. The interactions between the risk factors require more integrated risk measures that provide the portfolio managers a better view of the portfolio total risk. Experienced portfolio managers can manually adjust the constraints on risk sensitivities, i.e. key rate duration and convexity, to obtain a better risk/return portfolio by evaluating the risk measure after the optimization is completed. This iterative process may take approximately two weeks or more and yields suboptimal solutions.
Due to complexities of the risk and its impact on the portfolios, improvements are needed on the risk measures in addition to the conventional variance measure. Risk measures should provide additional information about the distribution of the portfolio values. The portfolio managers want to manage the risk caused by rare events, i.e., downside risk. A simulation technique is generally used to generate the distribution of the portfolio value based on a set of possible scenarios. The technique requires a significant amount of computation. Therefore, the simulation approach is mostly used to serve risk measurement rather than risk optimization purposes. Scenario-based optimization approach, which is based on the simulation technique, requires at least as much computational time as the simulation technique. Moreover, it is limited to only linear risk functions.
The invention addresses the above problems, as well as other problems, that are present in conventional techniques.